Due to space constraints, we focus without loss of generality on supervised learning, the currently most widely-used ML workflow [1]}. The task in supervised learning is to learn a function \(H\)  on the basis of some training data, which is a (typically very large) set of examples containing features and labels. Given the features, \(H\)  should ideally produce the labels. If the labels are from a continuous set, we call it a regression problem, if the labels are from a discrete set we call it a classification problem.
